3 rd International Lab Meeting – Summer session 2005

1. 3rd International Lab Meeting – Summer session 2005 • 11th Edition of the International Summer School of the • European Ph.D. on Social Representations and Communication • Social Representations in action and construction in Media and Society • “Applying the Facet Theory and Statistical Analysis • via HUDAP software to Research on Social Representations: • Theoretical and Methodological Computer Mediated Training Sessions” • at the European PhD on Social Representations & Communication • Multimedia LAB & Research Center • in Rome • Advantages and limitations of Facet Theory compared to other approaches • Prof. Dov Elizur • Dr. Eyal Yaniv

2. Advantages and limitations of Facet Theory compared to other approaches Prof. Dov Elizur Dr. Eyal Yaniv

3. Facet Theory versus other approaches • Strategy for constructing meaningful scientific theory • Guidelines for: • Systematic design of research • Formulation of hypotheses, regional hypotheses • Designing observations to cover the content universe - Content sampling • Parsimony – number .of dimensions • Tools for data analysis and test structural hypotheses • Fruitfulness –intension and extension of research • Comparability of research - Cumulative science

4. Limitations • No clear guidelines how to discover the basic facets of a content universe. • Requires abstract thinking

5. We find various definitions for theory: We can recognize two elements: Concepts – defined by people Empirical observations – external to people Scientific Theory

6. Guttman’s definition for theory A theory is an hypothesis of a correspondence between a definitional system for a universe of observations and an aspect of the empirical structure of those observations, together with a rationale for such an hypothesis. Let us consider its components

7. Sets and set products Set - a well defined collection of objects A: weight - a1 light a2 heavy B: height - b1 short b2 tall Set product: A set whose elements are a combination of other sets. i.e a1b1 light and short a1b2 light and tall b2a1 heavy and short b2a2 heavy and tall

8. Cartesian space heavy a2b1 a2b2 A:weight a1b2 a1b1 light tall short B: height

9. Main components in social research • Population P • Stimuli (questions) on content universe C • Group of possible responses - range R Mapping of cartesian group: Population} {Content}  (Range)}

10. Mapping Sentence The mapping sentence describes the definitional framework for observations, includes the: • subjects • content facets • range and combines them together in a regular sentence

11. Examples: Work values Personal values Intelligence Achievement Motive Organizational culture and IT

12. VALUES • NORMATIVE STANDARDS TO JUDGE AND TO CHOOSE AMONG ALTERNATIVE MODES OF BEHAVIOR (KLUCKHOHN, 1952) • DESIRABLE OR IMPORTANT STATES, OBJECTS, GOALS, OR BEHAVIORS, TRANSCENDING SPECIFIC SITUATIONSAND APPLIED AS NORMATIVESTANDARDS … (Schwartz & Bilsky, 1987)

13. VALUES • ITEMS ESTIMATING THE IMPORTANCE OF A GOAL IN LIFE • AREA(y) and • RANGE ordered from very IMPORTANT to VERY UNIMPORTANTGuttman, 1982)) • WHAT ARE WORK VALUES?

14. DEFINING WORK VALUES TWO BASIC FACETS: • FACET A- MODALITY OF OUTCOME • a1 material or instrumental (pay, benefits) • a2 social, affective (colleagues, esteem) • a3 personal, cognitive, (ach., interest)

15. WORK VALUES • FACET B– SYSTEM-PERFORMANCE CONTINGENCY. • b1 system rewards, unrelated to performance (benefits, hours) • b2 rewards contingent upon performance (pay, recognition)

16. Mapping sentence of Personal Values in various Life Areas

17. SSA of Personal Values

18. Intelligence • Exercise • Define in your own words the term: intelligence • Propose tasks/tests to measure intelligence

19. Cartesian space of intelligence

20. Intelligence – Mapping Sentence {recall} Testee (x) performs a task requiring the {application} {inference} of an objective rule concerned with {verbal} material {spacial} {numerical} • {very correct} { : } performance {very incorrect

21. Similarity structure Analysis (SSA)structural analysis/ small space analysis • SSA space – geometric representation of the abstract content universe • Assumption: every item can be represented by a point in the space, and every point can represent specific item • The distance between the points is inversely related to the relations between the items. The higher the correlations the smaller the distance, and vice versa.

22. Achievement Motive • Fist observed by projective techniques • Thematic Apperception Test (TAT) • David McClelland • Limitations: • Reliability – low • Consider as uonitary concept • Defining the Facets of Achievement Motive

24. A – Behavior Modality } { The assessment of a 1 ( Cognitive (belief employee (x) of the a 2 ( Affective (satisfaction behavior by a 3 ( Instrumental (action a 4 (Value (importance } a 5 ( Norm (desired { B – Referent b 1 Employee b 2 Colleagues Toward b 3 Supervisor b 4 Management } } { C - Objects High { c 1 Work c 2 IT IT Usage Low Organizational Culture and IT

25. The Empirical Structure of the Behavior toward Work (Creativity).A Two Dimensional SSA, separation index= 0.95 , coefficient of alienation = 0.14

26. The Empirical Structure of the Behavior toward IT. A Two Dimensional SSA, separation index= 0.88 , coefficient of alienation = 0.05

27. The Empirical Structure of the Behavior Toward Work and IT. A Two Dimensional SSA, coefficient of alienation = 0.17